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In SDN, forwarding devices can only operate correctly while connected to a logically centralized controller. To avoid single-point-of-failure, controller architectures are usually implemented as distributed systems. In this context, recent literature identified fundamental issues, such as device isolation and controller overload, and proposed controller placement strategies to tackle them. However, current proposals have crucial limitations: (i) device-controller connectivity is modeled using single paths, yet in practice multiple concurrent connections may occur; (ii) peaks in the arrival of new flows are only handled on-demand, assuming that the network itself can sustain high request rates; and (iii) failover mechanisms require predefined information, which, in turn, has been overlooked. This paper proposes Survivor, a controller placement strategy that addresses these challenges. The strategy explicitly considers path diversity, capacity, and failover mechanisms at network design. Comparisons to the state-of-the-art on survivable controller placement show that Survivor is superior because (a) path diversity increases the survivability significantly; and (b) capacity-awareness is essential to handle overload during both normal and failover states.

Monitoring tasks, such as anomaly and DDoS detection, require identifying frequent flow aggregates based on common IP prefixes. These are known as hierarchical heavy hitters (HHH), where the hierarchy is determined based on the type of prefixes of interest in a given application. The per packet complexity of existing HHH algorithms is proportional to the size of the hierarchy, imposing significant overheads.In this paper, we propose a randomized constant time algorithm for HHH. We prove probabilistic precision bounds backed by an empirical evaluation. Using four real Internet packet traces, we demonstrate that our algorithm indeed obtains comparable accuracy and recall as previous works, while running up to 62 times faster. Finally, we extended Open vSwitch (OVS) with our algorithm and showed it is able to handle 13.8 million packets per second. In contrast, incorporating previous works in OVS only obtained 2.5 times lower throughput.

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In recent years, as a result of the proliferation of non-elastic services and the adoption of novel paradigms, monitoring networks with high level of detail is becoming crucial to correctly identify and characterize situations related to faults, performance, and security. In-band Network Telemetry (INT) emerges in this context as a promising approach to meet this demand, enabling production packets to directly report their experience inside a network. This type of telemetry enables unprecedented monitoring accuracy and precision, but leads to performance degradation if applied indiscriminately using all network traffic. One alternative to avoid this situation is to orchestrate telemetry tasks and use only a portion of traffic to monitor the network via INT. The general problem, in this context, consists in assigning subsets of traffic to carry out INT and provide full monitoring coverage while minimizing the overhead. In this paper, we introduce and formalize two variations of the In-band Network Telemetry Orchestration (INTO) problem, prove that both are NP-Complete, and propose polynomial computing time heuristics to solve them. In our evaluation using real WAN topologies, we observe that the heuristics produce solutions close to optimal to any network in under one second, networks can be covered assigning a linear number of flows in relation to the number of interfaces in them, and that it is possible to minimize telemetry load to one interface per flow in most networks.

The demand of Virtual Reality (VR) video streaming to mobile devices is booming, as VR becomes accessible to the general public. However, the variability of conditions of mobile networks affects the perception of this type of high-bandwidth-demanding services in unexpected ways. In this situation, there is a need for novel performance assessment models fit to the new VR applications. In this paper, we present PERCEIVE, a two-stage method for predicting the perceived quality of adaptive VR videos when streamed through mobile networks. By means of machine learning techniques, our approach is able to first predict adaptive VR video playout performance, using network Quality of Service (QoS) indicators as predictors. In a second stage, it employs the predicted VR video playout performance metrics to model and estimate end-user perceived quality. The evaluation of PERCEIVE has been performed considering a real-world environment, in which VR videos are streamed while subjected to LTE/4G network condition. The accuracy of PERCEIVE has been assessed by means of the residual error between predicted and measured values. Our approach predicts the different performance metrics of the VR playout with an average prediction error lower than 3.7% and estimates the perceived quality with a prediction error lower than 4% for over 90% of all the tested cases. Moreover, it allows us to pinpoint the QoS conditions that affect adaptive VR streaming services the most.

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